Job Description
We are seeking a visionary Senior AI/ML Engineer to lead the next generation of intelligent systems at Nexus Horizon. In this pivotal role, you will architect scalable machine learning solutions that drive innovation across our product ecosystem. You will work at the intersection of data science and software engineering, deploying cutting-edge models that redefine user experiences. If you are passionate about the future of Artificial Intelligence and want to shape the technology landscape of tomorrow, we want to hear from you.
Why Join Us?
- Work with state-of-the-art Generative AI and Large Language Models (LLMs).
- Competitive compensation package and equity options.
- Flexible remote-first culture with a focus on work-life balance.
- Opportunity to mentor junior engineers and shape technical strategy.
Responsibilities
- Design, develop, and deploy robust machine learning models and pipelines using Python and modern frameworks like TensorFlow or PyTorch.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to translate business requirements into technical AI solutions.
- Optimize existing models for speed, scalability, and accuracy, ensuring real-time performance in production environments.
- Implement MLOps best practices to monitor model performance, retrain models, and manage version control.
- Stay abreast of the latest advancements in AI research and integrate novel techniques into our architecture.
- Conduct rigorous testing and validation of AI systems to ensure ethical usage and bias mitigation.
Qualifications
- Master’s or PhD degree in Computer Science, Statistics, Mathematics, or a related technical field.
- Minimum of 5 years of professional experience in machine learning, deep learning, or natural language processing.
- Strong proficiency in programming languages, specifically Python, with experience in SQL and NoSQL databases.
- Deep understanding of statistical analysis, algorithm design, and software engineering principles.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of shipping production-ready ML applications.